3. Unsupervised Learning

Clustering Basics — Quiz

Test your understanding of clustering basics with 5 practice questions.

Practice Questions

Question 1

What assumption does the k-means algorithm make about the shape and distribution of clusters in the data?

Question 2

In agglomerative hierarchical clustering using Ward’s linkage, which criterion is minimized when selecting clusters to merge?

Question 3

For a dataset of $n=50$ points and $k=3$ clusters, given between-cluster sum of squares $B=120$ and within-cluster sum of squares $W=80$, the Calinski–Harabasz index is closest to which value?

Question 4

In silhouette analysis, what does a silhouette score near zero for a data point indicate?

Question 5

Which essential step distinguishes the Gap Statistic method for selecting the number of clusters $k$?